Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

Abstract

Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.

Recurrent Multi-Fiber Network for 3D MRI Brain Tumor Segmentation

“…Taking advantage of AISA method, they achieved 94.7% of accuracy. Khan et al [24] introduced an automated multi-modal classification method using deep learning for brain tumor type classification. They initially employed the linear contrast stretching using edge-based histogram equalization and discrete cosine transform (DCT).…”

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

“…Alternating Direction Method of Multipliers (ADMM)-based CSC [47] is used to address the aforementioned two issues in (10) and (11). This completes the cartoon and texture decomposition phase and allows CSID to proceed to the next phase, which is detailed in the following subsection.…”

“…These images provide anatomical statistics [7]; however, the extraction of purposeful functional details from an individual image remains a critical issue. This demands multimodal image fusion, which integrates the complementary information of images from different modalities to produce an enhanced fused image through simulation, thereby providing enriched anatomical and functional information [6,7,[11][12][13].…”

Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination

“…To increase the conception rate of artificial insemination, a computer-aided method to determine the suitability (i.e., motility) of a sperm sample for artificial insemination based on the microscope footage is required. Recently, artificial intelligence (AI)-based methods, such as convolutional neural networks (CNNs) and deep learning combined with computer vision methods, were adopted for multiple biomedical imaging applications, such as disease classification [ 9 , 10 ], edge detection [ 11 ], image segmentation [ 12 ], knowledge inference [ 13 ], image reconstruction [ 14 ], shape recognition [ 15 ], and others [ 16 ].…”